Dual learning is a technique that leverages the symmetric structure of some learning problems such as machine translation.
[1]} [1]} focuses on the unsupervised setting and proposes to assist the learning of a translation model by exploiting the distortion between a sentence and its back-translation.
Dual supervised learning [3]} conducts joint learning from labeled data by adding an additional probabilistic constraint.
Model-level dual learning [4]}, from another aspect, exploits the duality of tasks in model level.
It designs an architecture for primal and dual tasks, and ties the model parameters playing similar roles.
